In a communications network, such as a cellular communications network, local parameter settings of one communication node oftentimes influence the selection of local parameters of neighboring communication nodes in the communications network. For instance, it is sometimes necessary for each base station in a cellular communications network to select a set of parameters that is uniquely distinguishable from those selected by its neighboring base stations (i.e., base stations that serve neighboring cells in the cellular communications network). Take downlink transmission for example. Each base station needs to transmit a locally unique Reference Signal (RS) for User Equipments (UEs) to identify the base station and to synchronize to the downlink transmission from the base station. The set of available reference signals is limited, and each base station needs to select (or be assigned) a reference signal that is different from the reference signals of its neighboring base stations. As another example, each base station may select (or be assigned) one of several frequency bands for transmission. If the same frequency band is only reused by other base stations serving cells that are far away, inter-cell interference can be significantly reduced. This is the classical frequency planning commonly practiced in second generation networks such as Global System for Mobile Communications (GSM) networks. There are also occasions when each base station may need to set a value to a parameter, such as transmit power, in such a way that the setting is compatible with those of the neighboring base stations in order to achieve a certain notion of optimality of the entire cellular communications network. These are just some typical problems encountered in the design of a cellular communications network in which a local parameter setting influences and is influenced by the settings of the neighboring cells.
Some of these problems, such as RS and frequency reuse, are typically static in nature and can therefore be solved by advanced planning during the build out of the cellular communications network. Currently, the parameters are set by planning tools that have access to information such as base station locations and radio propagation characteristics. Once a solution that is optimal network-wise is found, it remains unchanged for a long time until the deployment changes. However, other problems, such as transmit power control or precoder/beam selection, are more dynamic in nature and require more frequent updates as the channel conditions vary.
Further, in order to facilitate flexible, dense deployment of small base stations in future cellular communications networks, there is an increased interest in methods of coordinating parameters among neighboring cells in an autonomous and distributed fashion without a central controller, as any unplanned addition (or removal) of base stations can substantially alter the system topology and thus the preferred settings for the parameters. If a central controller is available, this entity may gather the needed information of all the communication nodes in the communications network through a backhaul to perform a joint optimization. On the other hand, without the need of such a central controller, the set of parameters can be coordinated in a distributed and selfish manner, that is, each communication node can make a decision to optimize its local performance. Lying between these two techniques, factor graphs may reach a solution close to the globally optimal but in a distributed manner through the use of message-passing algorithms.
Some applications of factor graphs for coordination of discrete parameters in a wireless communications network have been recently proposed, e.g. for the problem of fast beam coordination among base stations in Illsoo Sohn et al., “A Graphical Model Approach to Downlink Cooperative MIMO Systems,” 2010 IEEE Global Telecommunications Conference (GLOBECOM 2010), pp. 1-5, Dec. 6-10, 2010 (hereinafter “Sohn”) and Boon Loong Ng et al., “Distributed Downlink Beamforming With Cooperative Base Stations,” IEEE Transactions on Information Theory, Vol. 54, No. 12, pp. 5491-5499, December 2008 (hereinafter “Ng”). The basic idea in those works is to model the relationship between the local parameters to be coordinated among different communication nodes of a network and their respective performance metrics or costs using a factor graph. In Sohn, the belief propagation algorithm is adopted to solve the downlink transmit beamforming problem in a multi-cell Multiple-Input-Multiple-Output (MIMO) system considering a one-dimensional cellular communications network model. Since the approach in Sohn considers a one-dimensional cellular communications network, it does not consider all interfering cells. In Ng, some message-passing algorithms (including the sum-product algorithm) are deployed to coordinate parameters of downlink beamforming in a distributed manner in a multi-cell Single-Input-Single-Output (SISO) system. However, the approach in Ng cannot support base stations and mobile devices with multiple antennas. Further, both of the approaches proposed in Sohn and Ng do not account for the fact that the adopted message-passing algorithms do not always converge.
U.S. Patent Application Publication No. 2013/0142078 filed on Dec. 1, 2011 and published on Jun. 6, 2013 and U.S. Patent Application Publication No. 2013/0142082 filed on Nov. 29, 2012 and published on Jun. 6, 2013 describe systems and methods for distributed parameter coordination that, among other things, overcome the problems with the approaches described above. A particular application of one embodiment of the factor-based approach described in U.S. Patent Application Publication No. 2013/0142078 and U.S. Patent Application Publication No. 2013/0142082 for precoder selection in a multi-cell scenario is described in Igor M. Guerreiro et al., “A distributed approach to precoder selection using factor graphs for wireless communication networks,” EURASIP Journal on Advances in Signal Processing, Vol. 2013, No. 83, April 2013 (hereinafter “Guerreiro”).